Milvus Has a Free API You Should Know About

Milvus is a cloud-native vector database built for billion-scale similarity search. It's the go-to choice when your vector data exceeds what smaller databases can handle.

Why Milvus at Scale

A recommendation engine needed to search 500 million product embeddings in real-time. Most vector databases buckled under the load. Milvus handled it with 10ms p99 latency.

Key Features:

• Billion-Scale — Handle billions of vectors efficiently
• Multiple Index Types — IVF, HNSW, DiskANN, GPU indexes
• Hybrid Search — Vector + scalar filtering
• Partitioning — Organize data for efficient querying
• Cloud-Native — Kubernetes-native with horizontal scaling

Quick Start

pip install pymilvus

from pymilvus import MilvusClient

client = MilvusClient("milvus_demo.db")  # Lite mode - no server needed!

client.create_collection(
    collection_name="articles",
    dimension=384
)

client.insert(
    collection_name="articles",
    data=[{"id": 1, "vector": embedding, "title": "AI Guide"}]
)

results = client.search(
    collection_name="articles",
    data=[query_vector],
    limit=5
)

Why Choose Milvus

1. Billion-scale — proven at massive data volumes
2. Multiple indexes — choose the right tradeoff
3. Milvus Lite — embedded mode for development

Check out Milvus docs to get started.

---

Need large-scale data? Check out my Apify actors or email [e-mail] for custom solutions.